A range of PrPM approaches have been proposed in the literature [1]}, [2]}, [3]}, [4]}, [5]}. These approaches consist of two components. The first component is a predictive model that estimates, for each ongoing case in a process, the probability that this case will end up in a desired (\(dout\) ) or an undesired (\(uout\) ) outcome (\(out\) ).Some approaches complement the prediction scores with a causal effect estimates [6]}, [2]}, [5]} to take into account the effectiveness of the interventions. However, the two components mentioned here are discernible also in these approaches.
The second component is an intervention policy, which determines if an intervention should be triggered for a given case, with the goal of optimizing a given gain function. This gain function generally considers the benefit of increasing the success rate (i.e., more desired outcomes) as well as the cost of the interventions.
